ByteDance-Seed
Official@bytedance-seed
Offers specialized engineering protocols for distributed deep learning training, model migration, and rigorous code quality enforcement within the VeOmni ecosystem.
Agent Skills by ByteDance-Seed
Showing 9 vetted skills indexed across 1 GitHub repositories.
ByteDance-Seedveomni-new-model
Integrate HuggingFace models into VeOmni with patches and configuration.
ByteDance-Seedveomni-profile
Profile deep learning training runs by parsing Chrome traces and memory snapshots.
ByteDance-Seedveomni-debug
Debug distributed training errors with structured investigation protocols.
ByteDance-Seedveomni-migrate-transformers-v5
Migrate transformer model code to the v5 patchgen framework.
ByteDance-Seedveomni-uv-update
Update uv, torch, and related dependencies across VeOmni configuration files and Docker images.
ByteDance-Seedveomni-develop
Evaluate and refactor VeOmni components with impact analysis and safety rules.
ByteDance-Seedcreate-pr
Automate pull request creation by analyzing git status and generating standardized draft files.
ByteDance-Seedveomni-review
Analyze git diffs against constraint files to detect risks and violations.
ByteDance-Seedveomni-new-op
Assists adding, testing and documenting new operators in the SSA framework.
Frequently Asked Questions About ByteDance-Seed
FAQPage SchemaWhat specific tasks does ByteDance-Seed enable for machine learning engineers?▼
Engineers use these capabilities to profile deep learning training runs, debug distributed training errors, migrate transformer models to v5 frameworks, and integrate custom operators into the SSA framework while maintaining strict code quality standards.
Which technical personas benefit from using these engineering protocols?▼
These protocols are designed for machine learning infrastructure engineers, distributed systems researchers, and software developers working within the VeOmni ecosystem who require structured investigation and standardized migration paths for complex model architectures.
What are the primary dependencies and prerequisites for implementing these skills?▼
Implementation requires an existing VeOmni environment, familiarity with the SSA framework, and current configurations for torch and uv dependencies. Users must ensure their local git environment is configured to support the standardized draft file generation and constraint validation processes.